A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea

Abstract

Monitoring tidal dynamics is imperative to disaster management because it requires a high level of precision to avert possible dangers. Good knowledge of the physical drivers of tides is vital to achieving such a precision. The Taehwa River in Ulsan City, Korea experiences tidal currents in the estuary that drains into the East Sea. The contribution of wind to tide prediction is evaluated by comparing tidal predictions using harmonic analysis and three deep learning models. Harmonic analysis is conducted on hourly water level data from 2010–2021 using the commercial pytides toolbox to generate constituents and predict tidal elevations. Three deep learning models of long short-term memory (LSTM), gated recurrent unit (GRU), and bi-directional lstm (BiLSTM) are fitted to the water level and wind speed to evaluate wind and no-wind scenarios. Results show that Taehwa tides are categorized as semidiurnal tides based on a computed form ratio of 0.2714 in a 24-h tidal cycle. The highest tidal range of 0.60 m is recorded on full moon spring tide indicating the significant lunar pull. Wind effect improved tidal prediction NSE of optimal LSTM model from 0.67 to 0.90. Knowledge of contributing effect of wind will inform flood protection measures to enhance disaster preparedness.

Spatial distribution of microplastics ingested by Holothuria atra (Echinodermata: Holothuroidea) in the tourism and marine mariculture development zone, Karimunjawa, Indonesia

“…Here, we have applied charge sources and certain properties. Input data criteria included digitizing high‐resolution satellite coastlines, where the acquisition from ESRI GeoEye imagery 2019, bathymetry from the Geospatial Information Agency of Indonesia 2019, wind data recorded in hourly intervals obtained from ECMWF 2019 and tidal prediction data from analyzed harmonic constituent with the least square method (Kareem et al., 2022)…”

“…Here, we have applied charge sources and certain properties. Input data criteria included digitizing high-resolution satellite coastlines, where the acquisition from ESRI GeoEye imagery 2019, bathymetry from the Geospatial Information Agency of Indonesia 2019, wind data recorded in hourly intervals obtained from ECMWF 2019 and tidal prediction data from analyzed harmonic constituent with the least square method (Kareem et al, 2022) This model is a general numerical model system for streams in estuaries, bays, beaches, and seas. This model simulates variations in water level elevation and flow occurred due to various external force factors, such as bottom shear stress, wind shear stress, barometric pressure gradients, Coriolis force, momentum dispersion, sources and sinks, evaporation, flooding, and drying, wave radiation stresses (Siagian et al, 2014).…”